Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffe...Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to misjudgment.To tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the proposed method outperformed popular deep CNNs and existing prevalent attention models for smoke and fire detection problems.展开更多
This paper puts forward a rigorous approach for a sensitivity analysis of stochastic user equilibrium with the elastic demand （SUEED） model. First, proof is given for the existence of derivatives of output variables...This paper puts forward a rigorous approach for a sensitivity analysis of stochastic user equilibrium with the elastic demand （SUEED） model. First, proof is given for the existence of derivatives of output variables with respect to the perturbation parameters for the SUEED model. Then by taking advantage of the gradient-based method for sensitivity analysis of a general nonlinear program, detailed formulae are developed for calculating the derivatives of designed variables with respect to perturbation parameters at the equilibrium state of the SUEED model. This method is not only applicable for a sensitivity analysis of the logit-type SUEED problem, but also for the probit-type SUEED problem. The application of the proposed method in a numerical example shows that the proposed method can be used to approximate the equilibrium link flow solutions for both logit-type SUEED and probit-type SUEED problems when small perturbations are introduced in the input parameters.展开更多
We present VPVnet,a deep neural network method for the Stokes’equa-tions under reduced regularity.Different with recently proposed deep learning meth-ods[40,51]which are based on the original form of PDEs,VPVnet uses...We present VPVnet,a deep neural network method for the Stokes’equa-tions under reduced regularity.Different with recently proposed deep learning meth-ods[40,51]which are based on the original form of PDEs,VPVnet uses the least square functional of thefirst-order velocity-pressure-vorticity(VPV)formulation([30])as loss functions.As such,onlyfirst-order derivative is required in the loss functions,hence the method is applicable to a much larger class of problems,e.g.problems with non-smooth solutions.Despite that several methods have been proposed recently to reduce the regularity requirement by transforming the original problem into a corresponding variational form,while for the Stokes’equations,the choice of approximating spaces for the velocity and the pressure has to satisfy the LBB condition additionally.Here by making use of the VPV formulation,lower regularity requirement is achieved with no need for considering the LBB condition.Convergence and error estimates have been established for the proposed method.It is worth emphasizing that the VPVnet method is divergence-free and pressure-robust,while classical inf-sup stable mixedfinite elements for the Stokes’equations are not pressure-robust.Various numerical experiments including 2D and 3D lid-driven cavity test cases are conducted to demon-strate its efficiency and accuracy.展开更多
基金supported by the National Key Research and Development Program of China(Grant No. 2021ZD0112302)the National Natural Science Foundation of China(Grant Nos. 62076013, 62021003, 61890935)CAAI-Huawei MindSpore Open Fund(Grant No. CAAIXSJLJJ-2021-016A)。
文摘Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to misjudgment.To tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the proposed method outperformed popular deep CNNs and existing prevalent attention models for smoke and fire detection problems.
基金The Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX13_110)the Young Scientists Fund of National Natural Science Foundation of China(No.51408253)the Young Scientists Fund of Huaiyin Institute of Technology(No.491713328)
文摘This paper puts forward a rigorous approach for a sensitivity analysis of stochastic user equilibrium with the elastic demand （SUEED） model. First, proof is given for the existence of derivatives of output variables with respect to the perturbation parameters for the SUEED model. Then by taking advantage of the gradient-based method for sensitivity analysis of a general nonlinear program, detailed formulae are developed for calculating the derivatives of designed variables with respect to perturbation parameters at the equilibrium state of the SUEED model. This method is not only applicable for a sensitivity analysis of the logit-type SUEED problem, but also for the probit-type SUEED problem. The application of the proposed method in a numerical example shows that the proposed method can be used to approximate the equilibrium link flow solutions for both logit-type SUEED and probit-type SUEED problems when small perturbations are introduced in the input parameters.
基金supported by China National Natural Science Foundation(No.12001306)Guangdong Provincial Natural Science Foundation(No.2017A030310285)funded in part by Beijing Academy of Artificial Intelligence.
文摘We present VPVnet,a deep neural network method for the Stokes’equa-tions under reduced regularity.Different with recently proposed deep learning meth-ods[40,51]which are based on the original form of PDEs,VPVnet uses the least square functional of thefirst-order velocity-pressure-vorticity(VPV)formulation([30])as loss functions.As such,onlyfirst-order derivative is required in the loss functions,hence the method is applicable to a much larger class of problems,e.g.problems with non-smooth solutions.Despite that several methods have been proposed recently to reduce the regularity requirement by transforming the original problem into a corresponding variational form,while for the Stokes’equations,the choice of approximating spaces for the velocity and the pressure has to satisfy the LBB condition additionally.Here by making use of the VPV formulation,lower regularity requirement is achieved with no need for considering the LBB condition.Convergence and error estimates have been established for the proposed method.It is worth emphasizing that the VPVnet method is divergence-free and pressure-robust,while classical inf-sup stable mixedfinite elements for the Stokes’equations are not pressure-robust.Various numerical experiments including 2D and 3D lid-driven cavity test cases are conducted to demon-strate its efficiency and accuracy.
